suppressMessages(library(Signac))
suppressMessages(library(Seurat))
suppressMessages(library(tidyverse))
suppressMessages(library(data.table))
suppressMessages(library(RColorBrewer))
suppressMessages(library(readxl))
library(harmony)
library(SeuratWrappers)

Loading required package: Rcpp

panc <- readRDS('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/17_Mouse_Merged_normalized_SBR_2_10_26_weeks.rds')

setwd('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/upd.cell.type/')

DimPlot(panc, group.by = 'seurat_clusters', label = TRUE, raster = T)
ggsave('Dimplot_clusters.pdf', width = 7, height = 5)

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

options(repr.plot.width=8, repr.plot.height=7)
DimPlot(panc, group.by = 'Timepoint.weeks', label = TRUE, raster = T)
DimPlot(panc, group.by = 'Cohort', label = TRUE, raster = T)

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

panc$Mouse_ID %>% table()

.
 7248  7249  7251  7254  7259  7260  7352  7353  7354  7357  7361  7434  7435 
 6660  8171  5120  5231  2861  6391 12937 16472 18134 15188  9480 11497  7887 
 7448  7450  7452  7548 
 7615  6873  9773 12322 

options(repr.plot.width=8, repr.plot.height=7)

panc$predicted_doublet[is.na(panc$predicted_doublet)] <- NA
DimPlot(object = panc, group.by = 'predicted_doublet',cols = c('black', 'yellow'), raster = T,
        label=FALSE,label.size=6)
ggsave( "Dimplot_predicted_doublets.pdf", 
       height=6,width=8,useDingbats=FALSE,limitsize = FALSE)


options(repr.plot.width=14, repr.plot.height=7)

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

degs <- fread('/diskmnt/Projects/Users/allakarpova/Data/markers/Liver_mouse_markers.txt')

genes2plot <- degs$`Gene symbol` %>% unique()
p <- DotPlot(object = panc, group.by = 'seurat_clusters', features = genes2plot, col.min = 0, dot.min = 0.01, assay = 'RNA' )
p$data <- merge(p$data, degs[,c(1,5)], by.x = 'features.plot', by.y = 'Gene symbol', all.x=T)
p <- p  + RotatedAxis()
p <- p + facet_wrap(~ Cell.type , scales = "free",  drop = T, ncol = 3)
p <- p + theme(panel.spacing = unit(0, "lines"),
               strip.background = element_blank(),
               panel.border = element_rect(colour = "black"),
               panel.grid.major = element_line(colour = "grey80"),
               strip.text.x = element_text(angle = 00, vjust = 0.5, hjust = 0.5, size = 14),
               axis.text.x = element_text(size = 15, face = "bold", angle = 90, hjust = 1, vjust = 0.5),
               strip.placement = "outside")
p <- p + scale_color_viridis_c("viridis", direction = 1) +
    scale_size_area(limits=c(0,40), oob=scales::squish)
ggsave('Dotplot_markers.pdf', width = 30, height =30, useDingbats = F)


options(repr.plot.width=14, repr.plot.height=24)
p

Warning message in FetchData.Seurat(object = object, vars = features, cells = cells):
“The following requested variables were not found: Gstp-ps, Mug-ps1, mt-Rnr1, mt-Rnr2, Sdpr, Cyr61”
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
Warning message:
“Removed 1424 rows containing missing values or values outside the scale range (`geom_point()`).”
Warning message:
“Removed 1424 rows containing missing values or values outside the scale range (`geom_point()`).”

mouse_human_genes <- read.csv("http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt",sep="\t")

# separate human and mouse 
mouse <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[2]]
human <- split.data.frame(mouse_human_genes,mouse_human_genes$Common.Organism.Name)[[1]]

# remove some columns
mouse <- mouse[,c(1,4)]
human <- human[,c(1,4)]

mh_data <- merge.data.frame(mouse,human,by = "DB.Class.Key",all.y = TRUE, suffixes = c('.mouse', '.human')) 
head(mh_data)

myeloid.genes <- fread('/diskmnt/Projects/Users/allakarpova/Data/markers/Cell_state_markers_v10172022.txt', data.table = F, header = T)


myeloid.genes <- myeloid.genes %>% left_join(mh_data[,-1], by=c('Gene'='Symbol.human'))
genes2plot <- myeloid.genes$Symbol.mouse %>% unique()
p <- DotPlot(object = panc, group.by = 'seurat_clusters', features = genes2plot, col.min = 0, dot.min = 0.01, assay = 'RNA', cluster.idents = TRUE)

p$data <- merge(p$data, myeloid.genes[c(1:3, 6)], by.x = 'features.plot', by.y = 'Symbol.mouse', all.x=T)
p <- p  + RotatedAxis()
p <- p + facet_wrap(~Gene_set_group + Gene_set , scales = "free",  drop = T, ncol = 7)
p <- p + theme(panel.spacing = unit(0, "lines"),
               strip.background = element_blank(),
               panel.border = element_rect(colour = "black"),
               panel.grid.major = element_line(colour = "grey80"),
               strip.text.x = element_text(angle = 00, vjust = 0.5, hjust = 0.5, size = 14),
               axis.text.x = element_text(size = 15, face = "bold", angle = 90, hjust = 1, vjust = 0.5),
               strip.placement = "outside")
p <- p + scale_color_viridis_c("viridis", direction = 1) +
    scale_size_area(limits=c(0,40), oob=scales::squish)

ggsave(paste0( "Dotplot_marker_human_gene_expression_RNA.pdf"),plot = p, 
       height=180,width=50,useDingbats=FALSE,limitsize = FALSE)

A data.frame: 6 × 3

	DB.Class.Key	Symbol.mouse	Symbol.human
	<int>	<chr>	<chr>
1	48503904	Aldh1l1	ALDH1L1
2	48503905	Sry	SRY
3	48503906	Fry	FRY
4	48503907	Rpe65	RPE65
5	48503908	Ywhae	YWHAE
6	48503909	Camkv	CAMKV

Warning message in left_join(., mh_data[, -1], by = c(Gene = "Symbol.human")):
“Each row in `x` is expected to match at most 1 row in `y`.
ℹ Row 7 of `x` matches multiple rows.
ℹ If multiple matches are expected, set `multiple = "all"` to silence this warning.”
Warning message in FetchData.Seurat(object = object, vars = features, cells = cells):
“The following requested variables were not found (10 out of 29 shown): NA, Mrgpra7, Mrgpra5, H2-Ea, Vpreb1b, Vpreb1a, Ighg2a, Ighg, Siglecl2, Cd200r1l”
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
Warning message:
“Removed 41776 rows containing missing values or values outside the scale range (`geom_point()`).”

degs <- read_excel('/diskmnt/Projects/Users/allakarpova/Data/markers/41467_2018_6318_MOESM5_ESM_formatted.xlsx', 
                   col_names = T)
degs <- degs %>% left_join(mh_data[,-1], by=c('Gene'='Symbol.human'))
degs
genes2plot <- degs$Symbol.mouse %>% unique()
p <- DotPlot(object = panc, group.by = 'seurat_clusters', features = genes2plot, col.min = 0, dot.min = 0.01, assay = 'RNA', cluster.idents = TRUE)
p$data <- merge(p$data, degs[c(4,3)], by.x = 'features.plot', by.y = 'Symbol.mouse', all.x=T)
p <- p  + RotatedAxis()
p <- p + facet_wrap(~ Cell_type , scales = "free",  drop = T, ncol = 3)
p <- p + theme(panel.spacing = unit(0, "lines"),
               strip.background = element_blank(),
               panel.border = element_rect(colour = "black"),
               panel.grid.major = element_line(colour = "grey80"),
               strip.text.x = element_text(angle = 00, vjust = 0.5, hjust = 0.5, size = 14),
               axis.text.x = element_text(size = 15, face = "bold", angle = 90, hjust = 1, vjust = 0.5),
               strip.placement = "outside")
p <- p + scale_color_viridis_c("viridis", direction = 1) +
    scale_size_area(limits=c(0,40), oob=scales::squish)
ggsave(paste0( "Dotplot_20human_liver_paper_markers_gene_expression_RNA.pdf"),
       plot = p, height=70,width=20,useDingbats=FALSE,limitsize = FALSE)

Warning message in left_join(., mh_data[, -1], by = c(Gene = "Symbol.human")):
“Each row in `x` is expected to match at most 1 row in `y`.
ℹ Row 14 of `x` matches multiple rows.
ℹ If multiple matches are expected, set `multiple = "all"` to silence this warning.”

A tibble: 536 × 4

Gene	Cluster	Cell_type	Symbol.mouse
<chr>	<chr>	<chr>	<chr>
HSD11B1	Cluster1	Hep1	Hsd11b1
APOM	Cluster1	Hep1	Apom
PON3	Cluster1	Hep1	Pon3
TTC36	Cluster1	Hep1	Ttc36
BCHE	Cluster1	Hep1	Bche
F10	Cluster1	Hep1	F10
APOC4-APOC2	Cluster1	Hep1	NA
GOLT1A	Cluster1	Hep1	Golt1a
ADH1A	Cluster1	Hep1	Adh1
AQP9	Cluster1	Hep1	Aqp9
G6PC	Cluster1	Hep1	NA
PROX1	Cluster1	Hep1	Prox1
RCAN1	Cluster1	Hep1	Rcan1
HAMP	Cluster1	Hep1	Hamp2
HAMP	Cluster1	Hep1	Hamp
GPD1	Cluster1	Hep1	Gpd1
CTH	Cluster1	Hep1	Cth
TKFC	Cluster1	Hep1	Tkfc
G0S2	Cluster1	Hep1	G0s2
GHR	Cluster1	Hep1	Ghr
ABCC2	Cluster1	Hep1	Abcc2
CD2	Cluster2	a/b T-cells	Cd2
TRAC	Cluster2	a/b T-cells	Trac
CD3D	Cluster2	a/b T-cells	Cd3d
CCL5	Cluster2	a/b T-cells	Ccl5
TRBC2	Cluster2	a/b T-cells	Trbc1
TRBC2	Cluster2	a/b T-cells	Trbc2
GZMA	Cluster2	a/b T-cells	Gzma
CD3E	Cluster2	a/b T-cells	Cd3e
IL7R	Cluster2	a/b T-cells	Il7r
⋮	⋮	⋮	⋮
PRDX2	Cluster19	Erythroid cells	Prdx2
GABARAPL2	Cluster19	Erythroid cells	Gabarapl2
RAD23A	Cluster19	Erythroid cells	Rad23a
FBXO7	Cluster19	Erythroid cells	Fbxo7
CA2	Cluster19	Erythroid cells	Car2
HBD	Cluster19	Erythroid cells	Hbb-bh2
HBD	Cluster19	Erythroid cells	Hbb-bt
HBD	Cluster19	Erythroid cells	Hbb-bs
HBD	Cluster19	Erythroid cells	Hbb-b2
HBD	Cluster19	Erythroid cells	Hbb-b1
ACTA2	Cluster20	Hepatic stellate cells	Acta2
DCN	Cluster20	Hepatic stellate cells	Dcn
MYL9	Cluster20	Hepatic stellate cells	Myl9
COL3A1	Cluster20	Hepatic stellate cells	Col3a1
COL1A2	Cluster20	Hepatic stellate cells	Col1a2
COL1A1	Cluster20	Hepatic stellate cells	Col1a1
OLFML3	Cluster20	Hepatic stellate cells	Olfml3
TAGLN	Cluster20	Hepatic stellate cells	Tagln
BGN	Cluster20	Hepatic stellate cells	Bgn
TPM2	Cluster20	Hepatic stellate cells	Tpm2
COLEC11	Cluster20	Hepatic stellate cells	Colec11
LUM	Cluster20	Hepatic stellate cells	Lum
NEXN	Cluster20	Hepatic stellate cells	Nexn
COL14A1	Cluster20	Hepatic stellate cells	Col14a1
GGT5	Cluster20	Hepatic stellate cells	Ggt5
IGFBP7	Cluster20	Hepatic stellate cells	Igfbp7
ASPN	Cluster20	Hepatic stellate cells	Aspn
COL6A2	Cluster20	Hepatic stellate cells	Col6a2
COL4A2	Cluster20	Hepatic stellate cells	Col4a2
THBS2	Cluster20	Hepatic stellate cells	Thbs2

Warning message in FetchData.Seurat(object = object, vars = features, cells = cells):
“The following requested variables were not found (10 out of 21 shown): NA, Golt1a, Lyz3, H2-Ea, S100a11-ps, Cstdc3, Tmsb10b, Clec7a, Idi1-ps1, Ighg2a”
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Scale for size is already present.
Adding another scale for size, which will replace the existing scale.
Warning message:
“Removed 10671 rows containing missing values or values outside the scale range (`geom_point()`).”

myeloid.genes

A data.frame: 1398 × 6

Gene_set_group	Gene_set	Gene	Expression_direction	Common_name	Symbol.mouse
<chr>	<chr>	<chr>	<chr>	<chr>	<chr>
Immune	TAMs	PIK3CG			Pik3cg
Immune	TAMs	ARG1			Arg1
Immune	TAMs	IL6			Il6
Immune	TAMs	IL10			Il10
Immune	TAMs	PDGFB			Pdgfb
Immune	TAMs	TGFB1			Tgfb1
Immune	TAMs	CCL2			Ccl12
Immune	TAMs	CCL2			Ccl2
Immune	TAMs	IL23A			Il23a
Immune	TAMs	IL17A			Il17a
Immune	TAMs	CD163			Cd163
Immune	TAMs	MRC1			Mrc1
Immune	TAMs	MMP2			Mmp2
Immune	TAMs	MMP9			Mmp9
Immune	TAMs	VTCN1			Vtcn1
Immune	Pan-Immune	PTPRC		CD45	Ptprc
Immune	Granulocyte	FUT4		CD15	Fut4
Immune	Granulocyte	CLC			NA
Immune	Granulocyte	MS4A3			Ms4a3
Immune	Granulocyte	PI3			NA
Immune	Granulocyte	TCN1			NA
Immune	Granulocyte	CPA3			Cpa3
Immune	Granulocyte	HDC			Hdc
Immune	Granulocyte	GATA2			Gata2
Immune	Granulocyte	CXCL8			NA
Immune	Granulocyte	GATA1			Gata1
Immune	Granulocyte	MS4A2			Ms4a2
Immune	Granulocyte	SIGLEC8			Siglecf
Immune	Granulocyte	SIGLEC8			Siglece
Immune	Mast	PLAU			Plau
⋮	⋮	⋮	⋮	⋮	⋮
Normal cells	Osteoblasts early	DLX5			Dlx5
Normal cells	Osteoblasts early	SATB2			Satb2
Normal cells	Osteoblasts early	ALPL			Alpl
Normal cells	Osteoblasts early	COL1A1			Col1a1
Normal cells	Osteoblasts early	COL1A2			Col1a2
Normal cells	Osteocyte	PDPN1			NA
Normal cells	Osteocyte	PHEX			Phex
Normal cells	Osteocyte	DMP1			Dmp1
Normal cells	Osteocyte	CAPG			Capg
Normal cells	Osteocyte	MEPE			Mepe
Normal cells	Osteocyte	SOST			Sost
Normal cells	Chondrocyte	SOX9			Sox9
Normal cells	Chondrocyte	COMP			Comp
Normal cells	Chondrocyte	ACAN			Acan
Normal cells	Chondrocyte	SOX5			Sox5
Normal cells	Chondrocyte	SOX6			Sox6
Normal cells	Mesothelial cells	KRT19			Krt19
Normal cells	Mesothelial cells	MSLN			Msln
Normal cells	Mesothelial cells	PDPN			Pdpn
Tumor	ccRCC_Tumor	CA9			Car9
Tumor	ccRCC_Tumor	CD24			Cd24a
Tumor	ccRCC_Tumor	LTL			NA
Tumor	ccRCC_Tumor	PAX2			Pax2
Tumor	ccRCC_Tumor	PAX8			Pax8
Tumor	ccRCC_Tumor	VEGFA			Vegfa
Tumor	ccRCC_Tumor	COL23A1			Col23a1
Tumor	ccRCC_Tumor	CP			Cp
Tumor	ccRCC_Tumor	ENPP3			Enpp3
Tumor	ccRCC_Tumor	NDRG1			Ndrg1
Tumor	ccRCC_Tumor	SHISA9			Shisa9

options(repr.plot.width=15, repr.plot.height=7)
panc@meta.data %>% ggplot(aes(x = seurat_clusters, fill = predicted_doublet)) +
    geom_bar(stat = 'count', position = 'fill')

doublet.clusters <- panc@meta.data %>% 
    group_by(seurat_clusters, predicted_doublet) %>% 
    tally() %>% 
    group_by(seurat_clusters) %>%
    mutate(pct = n/sum(n)) %>% 
    filter(predicted_doublet, pct > 0.5) %>% 
    pull(seurat_clusters)

doublet.clusters

1. 26
2. 31
3. 33
4. 34
5. 36
6. 37
7. 40
8. 42

Levels:

1. '0'
2. '1'
3. '2'
4. '3'
5. '4'
6. '5'
7. '6'
8. '7'
9. '8'
10. '9'
11. '10'
12. '11'
13. '12'
14. '13'
15. '14'
16. '15'
17. '16'
18. '17'
19. '18'
20. '19'
21. '20'
22. '21'
23. '22'
24. '23'
25. '24'
26. '25'
27. '26'
28. '27'
29. '28'
30. '29'
31. '30'
32. '31'
33. '32'
34. '33'
35. '34'
36. '35'
37. '36'
38. '37'
39. '38'
40. '39'
41. '40'
42. '41'
43. '42'
44. '43'
45. '44'
46. '45'
47. '46'
48. '47'
49. '48'
50. '49'

liver.cell.types <- c('Hepatocytes' = '#2F8AC4',
  'Cholangiocytes' = '#52BCA3',
  'Doublet' = 'yellow',
  'Central venous LSECs' = '#ED645A',
  'Periportal LSECs' = '#E58606', 
  'Portal endothelial cells' = '#DAA51B',
   'LSECs' = '#ED645A',
  'Lymphatic endothelial cells'='#99C945',
  'Noninflammatory macs' = '#5D69B1',
                      'Kupffer cells' = '#5D69B1',
  'Inflammatory macs' = '#764E9F',
 
  
  'Portal fibroblasts' = '#CC61B0',
  'Hepatic stellate cells' = '#CC3A8E',
  
  'vSMCs' = '#24796C',
  'Pericytes' = '#0F8554',
  'Mesothelial cells' = '#A5AA99',
  'NK cells' = '#ede5cf',
  'T-cells' = '#6b705c',
  'B-cells' = '#e0c2a2',
  'Plasma' = '#d39c83',
  'Mast' = '#c1766f',
  'pDC' = '#a65461',
  'Monocytes' = '#813753',
  'cDC2' = '#541f3f',
   'DC' = '#541f3f',
  'mregDC'= '#8C785D')

DefaultAssay(panc) = 'SCT'


panc@meta.data$cell_type_merged <- case_when(panc$predicted_doublet ~ 'Doublet',
                                   
                                     panc$seurat_clusters %in% doublet.clusters ~ 'Doublet',
                                     panc$seurat_clusters %in% c(26) ~ 'Doublet',
                                     panc$seurat_clusters %in% c(0,41, 14,17, 6,8,10,11, 20,21,27,2,9,19, 4,18,35, 28, 32) ~ 'Hepatocytes',
                                      panc$seurat_clusters %in% c(1,3) ~ 'Hepatic stellate cells',
                                             panc$seurat_clusters %in% c(38) ~ 'Pericytes',
                                      panc$seurat_clusters %in% c(5) ~ 'Kupffer cells',
                                             panc$seurat_clusters %in% c(23) ~ 'Inflammatory macs',
                                     panc$seurat_clusters %in% c(12,13,15) ~ 'Cholangiocytes',
                                             panc$seurat_clusters %in% c(7,16,22, 29) ~ 'LSECs',
                                             #panc$seurat_clusters %in% c(29) ~ 'Periportal LSECs',
                                             panc$seurat_clusters %in% c(24,43) ~ 'T-cells',
                                              panc$seurat_clusters %in% c(45) ~ 'Mesothelial cells',
                                              panc$seurat_clusters %in% c(30) ~ 'B-cells', # not plasma
                                             panc$seurat_clusters %in% c(25) ~ 'DC', #not B-cells
                                             panc$seurat_clusters %in% c(39) ~ 'cDC2', #not B-cells
                                             panc$seurat_clusters %in% c(32, 44) ~ 'Proliferating', 
                                             panc$seurat_clusters %in% c(46) ~ 'Adipocytes',
                                             panc$seurat_clusters %in% c(48) ~ 'Neurons',
                                             panc$seurat_clusters %in% c(47) ~ 'Skeletal muscle',
                                    TRUE ~ 'Unknown')
                                  


options(repr.plot.width=14, repr.plot.height=7)
DimPlot(object = panc,raster=T, group.by = 'cell_type_merged',cols = liver.cell.types,
        label=TRUE,label.size=4)
ggsave( "Dimplot_cell_type_merged.pdf",
       height=6,width=10,useDingbats=FALSE,limitsize = FALSE)

panc@meta.data %>% count(seurat_clusters, cell_type_merged) %>% filter(cell_type_merged=='Unknown')

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

A data.frame: 1 × 3

seurat_clusters	cell_type_merged	n
<fct>	<chr>	<int>
49	Unknown	71

fwrite((panc@meta.data %>% select(cell_type_merged)), 
       '17_Mouse_Merged_normalized_SBR_2_10_26_weeks_02032025.tsv', sep = '\t', row.names = TRUE)

mouse.liver.cell.types <- c('Hepatocytes' = '#2F8AC4',
                            'Proliferating' = "#a9d6e5",
                            'Cholangiocytes' = '#52BCA3',
                            'Central venous LSECs' = '#e92e20',
                            'Mid lobular LSECs' = '#ED645A',
                            'Periportal LSECs' = '#E58606', 
                            'Portal endothelial cells' = '#DAA51B',
                            'Vascular endothelial cells' = '#ffb703',
                            'Lymphatic endothelial cells'='#99C945',
                            
                            'Kupffer cells' = '#5D69B1',
                            'Inflammatory macs' = '#764E9F',
                            'Portal fibroblasts' = '#CC61B0',
                            'Hepatic stellate cells' = '#CC3A8E',
                            'Tumor fibroblasts' = '#94346E',
                            'vSMCs' = '#24796C',
                            'Pericytes' = '#0F8554',
                            'Mesothelial cells' = '#A5AA99',
                            'NK cells' = '#ede5cf',
                            'T-cells' = '#6b705c',
                            'B-cells' = '#e0c2a2',
                            'Plasma' = '#d39c83',
                            'Mast' = '#c1766f',
                            'pDC' = '#a65461',
                            'Monocytes' = '#813753',
                            'Adipocytes' = '#DCB0F2',
                            'Skeletal muscle' = '#C9DB74',
                            'Neurons' = '#E73F74',
                            'Unknown' = 'grey',
                            'cDC2' = '#541f3f',
                            'DC'= '#8C785D')

setwd('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/no_doublets')

obj <- readRDS('/diskmnt/Projects/SenNet_analysis/Main.analysis/merged/merge_SBR_RNA_1/no_doublets/17_Mouse_Merged_normalized_SBR_2_10_26_weeks_no_doublet.rds')

meta <- fread('/diskmnt/Projects/SenNet_analysis/Main.analysis/data_freeze/v1.1/metadata/17_Mouse_Merged_normalized_SBR_2_10_26_weeks_03052025.tsv', header = T) %>%
    column_to_rownames('V1')

head(meta)

A data.frame: 6 × 3

	cell_type_broad	cell_type_sen	cell_type_sen2
	<chr>	<chr>	<chr>
SR006342_sbr_7448_Male_10_AAACCCAAGAAGCCAC-1	Portal fibroblasts	Portal fibroblasts	Portal fibroblasts
SR006342_sbr_7448_Male_10_AAACCCAAGCCATTTG-1	T-cells	T-cells	T-cells
SR006342_sbr_7448_Male_10_AAACCCACACGCGGTT-1	Hepatic stellate cells	Activating HSCs	Activating HSCs
SR006342_sbr_7448_Male_10_AAACCCACATTAAGCC-1	Hepatocytes	Hepatocytes Zone 1/2	Hepatocytes
SR006342_sbr_7448_Male_10_AAACCCAGTATCCCAA-1	Cholangiocytes	Cholangiocytes	Cholangiocytes
SR006342_sbr_7448_Male_10_AAACCCATCAGAATAG-1	Cholangiocytes	Cholangiocytes	Cholangiocytes

obj <- AddMetaData(obj, meta)

options(repr.plot.width=15, repr.plot.height=10)

DimPlot(obj, group.by = 'cell_type_broad', cols = mouse.liver.cell.types, raster=T, label = TRUE) +
    coord_fixed()
ggsave('Dimplot_cell_type_broad.pdf', width = 13, height = 8, useDingbats = F)

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

dim(obj)

1. 25319
2. 142491